implementation

IVT Feature Calculation

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Linked challenge: Sub-seasonal Atmospheric River & Coastal Flood Prediction using GraphCast ML Integration

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Linked challenge

Sub-seasonal Atmospheric River & Coastal Flood Prediction using GraphCast ML Integration

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Original prompt text with formatting preserved for inspection.

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Write an Xarray function `calculate_ivt(ds)` that integrates specific humidity `q` and wind components `u`, `v` across pressure levels 1000hPa to 300hPa.

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Linked challenge

Sub-seasonal Atmospheric River & Coastal Flood Prediction using GraphCast ML Integration

El Niño weather shifts intensify atmospheric river corridors that deposit massive precipitation along coastal mountain ranges, causing flash flooding and widespread infrastructure damage. Physics-based global numerical weather prediction models require hours on supercomputers. Deep learning weather models like GraphCast provide sub-seasonal forecasts at fraction-of-a-second inference speeds. In this challenge, you will build an inference pipeline integrating deep learning weather predictions (GraphCast or ECMWF Open Data weather weights) with local hydrological runoff models. Your solution will process 3D atmospheric temperature/humidity pressure grids, predict integrated vapor transport (IVT), and forecast river gauge flood height triggers 10 days in advance.

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